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SummaryMobile devices are the primary communication tool in day to day life of the people. Nowadays, the enhancement of the mobile applications namely IoTApps and their exploitation in various domains like healthcare monitoring, home automation, smart farming, smart grid, and smart city are crucial. Though mobile devices are providing seamless user experience anywhere, anytime, and anyplace, their restricted resources such as limited battery capacity, constrained processor speed, inadequate storage, and memory are hindering the development of resource‐intensive mobile applications and internet of things (IoT)‐based mobile applications. To solve this resource constraint problem, a web service‐based IoT framework is proposed by exploiting fuzzy logic methodologies. This framework augments the resources of mobile devices by offloading the resource‐intensive subtasks from mobile devices to the service providing entities like Arduino, Raspberry PI controller, edge cloud, and distant cloud. Based on the recommended framework, an online Repository of Instructional Talk (RIoTalk) is successfully implemented to store and analyze the classroom lectures given by faculty in our study site. Simulation results show that there is a significant reduction in energy consumption, execution time, bandwidth utilization, and latency. The proposed research work significantly increases the resources of mobile devices by offloading the resource‐intensive subtasks from the mobile device to the service provider computing entities thereby providing Quality of Service (QoS) and Quality of Experience (QoE) to mobile users.

Due to the drastic exploitation of mobile devices and mobile apps in the day-to-day activities of people, the enhancement in hardware and software tools for mobile devices is also rising rapidly to cater to the requirements of mobile users. However, the progress of resource-intensive mobile applications is still inhibited by the limited battery power, restricted memory, and scarce resources of mobile devices. By employing mobile cloud computing, mobile edge computing, and fog computing, many researchers are providing their frameworks and offloading algorithms to augment the resources of mobile devices. In the existing solutions, offloading resource-intensive tasks is adopted only for specific scenarios and also not supporting the flexible exploitation of IoT-based smart mobile applications. So, a novel neuro-fuzzy modeling framework is proposed to augment the inadequate resources of a mobile device by offloading the resource-intensive tasks to external entities, and also a Bat optimization algorithm is exploited to schedule as many tasks as possible to the augmentation entities thereby improving the total execution time of all tasks and minimizing the resource exploitation of the mobile device. In this research work, external augmentation entities like distant cloud, edge cloud, and microcontroller devices are providing Resource augmentation as a Service (RaaS) to mobile devices. An IoT-based smart transport mobile app is implemented based on the proposed framework which depicts a significant reduction in execution time, energy consumption, bandwidth utilization, and average delay. Performance analysis depicts that the neuro-fuzzy hybrid model with Bat optimization provides a significant improvement compared with proximate computing and web service frameworks on the Quality of Service (QoS) parameters namely energy consumption, execution time, bandwidth utilization, and latency. Thus, the proposed framework exhibits a feasible solution of RaaS to resource-constrained mobile devices by exploiting edge computing.

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SummaryMobile devices play a vital role in people's day‐to‐day activities and are essential for communication, accessing Internet resources, entertainment, and so forth. In most cases, the performance of mobile applications is restricted due to the inadequate resources of mobile specifications such as controlled battery power, predetermined storage, and limited processing competence. Thus to improve the processing efficiency of resource‐constrained mobile devices, this research proposes a model developed on offloading technique based on the recent literature observations which transfer the resource‐consuming tasks from mobile devices to proximate computing entities. This generic architecture aiming for resource augmentation is formulated with five fundamental components including a mobile device, offloading engine, resource augmentation engine, scheduler, and synchronizer. The offloading engine identifies the resource‐intensive tasks that need to be executed on external entities using soft computing techniques such as neural network training algorithms, fuzzy logic, and neuro‐fuzzy logic processing. The augmentation engine chooses feasible edge computing entities such as Arduino, Raspberry PI controller, and fog devices. The scheduler prioritizes the resource‐consuming tasks for offloading based on their importance. The synchronizer coordinates all the central components by updating the execution status of tasks at regular intervals of time. Further extension of this research work, based on the proposed architecture three resource augmentation frameworks are developed namely (a) an adaptive proximate computing framework (b) a web service‐based IoT framework, and (c) a neuro‐fuzzy hybrid framework. Moreover, these frameworks are realized through the development of resource‐efficient intelligent edge computing IoT mobile apps namely (a) an inventory monitoring app (b) a lecture recording app, and (c) an intelligent transport app. The performance of these three resource augmentation frameworks is analyzed through the simulation results and it illustrates that the neuro‐fuzzy hybrid framework outperforms the other two. The outcome of the proposed research contributions improves the effectiveness of mobile communication by providing seamless services to mobile users regardless of their resource‐limited devices. Besides the devised edge computing architecture supports mobile application developers in the design and deployment of resource‐rich mobile applications in low‐specification mobile devices. Also, there is a strong scope for implementing the proposed generic architecture as an industrial smart product with adequate hardware and software configurations.